Areas Of Sparse Vegetation Research Articles

Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data.

Land use/land cover (LULC) change is an important indicator used for assessing the function and health of ecosystems. Understanding the patterns of LULC change assists in managing natural resources effectively, especially for regions where there are minimal or no reported data on the status of LULC. In this study, remotely sensed Landsat satellite imagery from 5years (i.e., 1988, 1996, 2002, 2008, and 2017), geographic information systems (GIS), and the hybrid cellular automata (CA)-Markov model were used to (i) quantify the past and present LULC changes and (ii) model the future changes in Sulaimani Province in the Kurdistan region of Iraq (KRI). To accomplish these objectives, five LULC maps with various class categories were generated using the maximum likelihood classifier (MCL). The classified maps for 1996, 2002, 2008, and 2017 were used in the hybrid model to simulate LULC maps for 2017 and 2037. The map simulated for 2017 was validated with the classified 2017 LULC map. The change analysis demonstrated that between 1988 and 2017, the built-up areas and agricultural fallow land increased by 419% and 226%, respectively. In the future predictions for 2037, built-up areas and agricultural fallow land showed increasing trends of 5.5% and 26.5%, respectively. In contrast, agricultural land, plantation land, and sparse vegetation areas were predicted to decrease by 29.4%, 65.8%, and 36.9%, respectively. In addition, in 2008, waterbodies shrank by 43.36% in comparison with their status in 1988, suggesting that 2008 was a severe drought year. These findings provide invaluable baseline information with which conservation biologists, agricultural engineers, urban planners, and decision makers can better manage natural resources and monitor environmental changes. Based on these results, sustainable development actions and an early warning system can be established.

Tracing land use and land cover change in peri-urban Delhi, India, over 1973–2017 period

Land use and land cover changes over 1973-2017 period in peripheral Delhi were mapped based on digital classification of satellite data and their driving forces ascertained. Urban area expanded and agricultural area diminished at annual rates of 38.6% and 2.1%, respectively, during the 1973-2017 period. Urban expansion occurred more in scrub and sparse vegetation areas than in cultivated lands or ponds. Loss of cultivated land happened mostly due to abandonment of cropping and tree planting in farmhouses developed by the urban elites. Improvement in the state of forests in terms of their expansion as well as densification offsets their loss due to urbanisation, encroachment and logging. The increment in the green cover was due to strict enforcement of compensatory afforestation/forest conservation law, growing demand of ecotourism, emergence of tree-clad farmhouses and increased environmental awareness and surveillance. This research will help in comprehending policies favouring sustainable urban development.

Constraining coseismic earthquake slip using Structure from Motion from fault scarp mapping (East Helanshan Fault, China)

This study uses fault scarps to investigate past earthquakes and the potential seismic hazard of the East Helanshan Fault (EHF). The EHF is a Holocene active normal fault located at the complex transition from compression in the northeastern corner of the Tibetan Plateau to apparently east-west extension in the west margin of Ordos Block, as implied by the Yinchuan Faulted Basin and its boundary normal faults. Repeated normal faulting events, including the 1739 CE event on the EHF, offset alluvial fans of Late Pleistocene and Holocene age and formed remarkable fault scarps, especially along its Suyukou and Hongguozi segments. We selected three fault sections in the Suyukou and Hongguozi segments, where fault scarps of different heights are well preserved on different terrace surfaces. By using Structure-from-Motion-Multiview Stereo (SfM-MVS) techniques on Unmanned Aerial System (UAS) imagery, we acquired high-quality topography of the selected fault sections. Detailed mapping and surveying of the offset geomorphic features and their associated fault scarps, with distinct free faces and multiple slope breaks, suggest that surface-rupturing earthquakes occurred repeatedly along the EHF. Densely spaced cross scarp topographic profiles were extracted to evaluate the vertical slip distributions using newly acquired SfM-derived topography. Through statistical analysis of the displacement dataset, we constrain multiple earthquake events and their cumulative offsets on the Suyukou and Hongguozi segments. Large earthquakes on the EHF produced different characteristic co-seismic slips on the Suyukou segment and the Hongguozi segment of ~2.6 m and ~1.7 m, respectively. The scaling relationships between the moment magnitude, surface rupture length, and the average co-seismic slip indicate possible magnitudes within a range of Mw 6.8–7.2, and a potential magnitude of approximately Mw 7.4 as an upper bound in the case of a multi-fault rupture along the EHF. This study demonstrates the UAS-based SfM photogrammetry as an effective means for mapping fault zone topography in areas of sparse vegetation, as well as its potential for identifying and measuring subtle geomorphic features associated with past earthquakes.

Increased Surface Broadband Emissivity Driven by Denser Vegetation on the Tibetan Plateau Grassland Area

Global changes are profoundly affecting the global terrestrial ecosystems, especially for the vegetation. Simultaneously, the affected vegetation gives feedback to the climates. The Tibetan Plateau (TP), one of the most sensitive areas to global changes, has undergone extraordinary changes on its ecosystem processes. In the multitudinous land surface ecosystem processes affecting the climate, the process of land surface energy balance affecting by vegetation activity is one of the most important and still has not been well recognized. The spatial and temporal patterns of the broadband emissivity (BBE) on the TP and its relations to the vegetation activity and land surface temperature were examined in this research. We find that elevated BBE is regulated by increasing vegetation activity for grasslands over the TP from 2000 to 2015. The spatial patterns of BBE and its interannual changes are highly correlated with vegetation activity. The BBE changing rate generally declines along rising elevation, due to the shrunk effects from vegetation activity. A greater sensitivity of BBE to vegetation activity occurs in the sparse vegetation area or high elevation zone than in the dense vegetation area or low elevation zone. Increasing BBE has a cooling effect on the land surface, especially at night. This cooling effect is related to wind speed. The growing season BBE trend as regulated by vegetation activity highlights the importance to take mounting notice of the growing season long-wave energy fluxes of surface energy balance studies in the future.

Comparative Analysis of Water-Energy Cycle Processes Based on High-Resolution Assimilation Dataset of the Water-Energy Cycle in China Data Over Different Underlying Surfaces in Qinghai-Tibet Plateau

We used a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC) to study the land-atmosphere interactions and meteorological characteristics in inhomogeneous underlying surface of the Qinghai-Tibet Plateau (QTP). Three different underlying surfaces (i.e., grassland, open shrubland, and barren or sparsely vegetated) of the QTP are selected and the meteorological elements on each underlying surface grid are averaged. We compared and analyzed the Green Vegetation Fraction (GVF), precipitation, soil moisture and soil temperature, and energy fluxes for three different land-use types in QTP. The results showed that the vegetation coverage of HRADC showed a gradual decrease trend from southeast to northwest throughout the Qinghai-Tibet Plateau. The GVF of the grassland in the southeast can reach more than 60% in summer, and only about 20% in sparse vegetation areas. HRADC can well reproduce the seasonal change trend of soil temperature and soil moisture in different underlying surfaces. The annual variation trend of soil temperature shows that the time of the deep soil temperature reaching the peak value lags behind the shallow layer. The annual averaged soil moisture over grassland is higher than that of open shrubland and barren land, which is consistent with the plateau precipitation distribution. The peak value of sensible heat flux over grassland is only 80 W·m-2 in April, and the latent heat flux can reach 90 W·m-2, and the net radiation of the barren land can reach 210 W·m-2 in July. This study is important to discover the water-energy cycle characteristics of QTP.

Spatial patterns of vegetation carbon sinks and sources under water constraint in Central Asia

The terrestrial ecosystem carbon cycle is a major contributing factor in the rise in global average temperature. Based on the net ecosystem production (NEP) calculation model and classifications, the spatial distribution patterns of carbon sources and sinks for vegetation in Central Asia are analyzed. The results show obvious zonal characteristics in the distribution of carbon sources and sinks. Carbon source areas form the majority and are mainly located in south-central Kazakhstan and parts of Uzbekistan and Turkmenistan, while most carbon sink areas are located in northern Kazakhstan, Kyrghyzstan and Tajikistan. NEP shows a downward trend during 2000–2015, indicating decreasing capacities of low and high carbon sinks in forest land, sparse vegetation, and bare areas. Even a weakening in the carbon sink capacity resulted in no significant decrease, indicating instead a decline in grassland quality. The change in NEP matches the change in terrestrial net primary productivity (NPP). Water is the limiting factor for ecological attributes in arid and semi-arid areas, with drought influencing both the carbon budget and balance. These conclusions are critical for maintaining ecological stability and sustainable economic development in the Silk Road Economic Belt.

Mapping of Mosquito Breeding Areas with Risk Levels u sing Decision Rules for Guntur Urban , Andhra Pradesh Based on Land u se Parame ters

Dengue is one of the prevalent diseases present even in this technology-driven society, diseases that are rampant in earlier as prevalent diseases are compromised can address through the latest research. Dengue is mainly caused due to Aedes Ageypti mosquito mostly breeds around the houses of the residents precisely with improper storage of water and ill-conditioned places that enhance the ability of the mosquito to maintain high breeding rate and prevail the disease. This study utilizes the land use/cover (LULC) application to develop the decision rules using Boolean overlay operations that can generate the risk maps using on the factors Dense Vegetation, Sparse Vegetation, Water bodies and urban area cover [19]. These risk factors are generated using polygon overlay and further categorized into high-risk, moderate, and low risk depending upon the vulnerability of the region. The study is conducted in the Guntur Urban of state Andhra Pradesh, wherein the LULC scenarios are investigated by using Google earth imagery [1] for Guntur urban, Andhra Pradesh.

Assessment of Landuse and Landcover Changes and its Implications to Flooding Along Omambala Flood Plain, Anambra State

This paper assessed the landuse and landcover changes and its implications to flooding along Omambala floodplain in Anambra State. The paper aimed at identifying the landuse and landcover types in Omambala floodplain in relations to their temporal dynamics, the extent of landuse change and its implication to recurring incidence of flood disaster in the study area over a period of 20 years starting from the year 1998 to 2018. Multi-temporal Landsat TM and ETM+ imageries were obtained at 10 years interval; 1998; 2008 and 2018 to observe the pattern of landuse and landcover along the floodplain over the period of study. Population data of study area were obtained from National Population Commission on four time paints as to understand the rate of land conversion and modification of the floodplain. Visual interpretation method was used to map landuse and landcover into five classes namely: waterbody; built-up; sparse vegetation; bare surface and dense vegetation. The result of the analysis of the imageries showed a considerable change in the pattern of landuse and landcover classes within the period under study. There was also a considerable population growth leading to progressive conversion of natural vegetation to other human activities with Built-up lands sparse vegetation and bare surface on the increase as well as their attendant consequences. However, the study concluded that various anthropogenic landuse activities especially poor farming system leading to increasing sparse vegetation, bare surface and built-up area are major factors that affects natural vegetation thereby worsening the incidence of flooding in the study area. The research therefore recommends relocation of settlements, greening of space already turning bare, effective floodplain management and continues monitoring of changes on land characters to ensure environmental sustainability and to forestall recurring incidence of flooding along the floodplain.

Coefficients optimization of the GLASS broadband emissivity based on FTIR and MODIS data over the Taklimakan Desert

In this paper, the Taklimakan Desert land surface emissivity measured by portable Fourier Transform thermal InfraRed spectroscopy (FTIR) was used to re-estimate Global LAnd Surface Satellite (GLASS) BroadBand Emissivity (BBE) and Moderate Resolution Imaging Spectroradiometer (MODIS) BBE optimal coefficients equations. In addition, the revised BBE equations of both GLASS and MODIS with the optimized coefficients were obtained. Comparing the FTIR and MODIS BBE data with the values retrieved before and after the revised GLASS BBE equation, it was found that the revised GLASS BBE equation is more accurate than the original one. First, according to the error analysis with FTIR data, the value of R2 is increased from 0.4 to 0.9, and the Root Mean Square Error (RMSE) and Bias are reduced by 1 and 3 magnitude orders, respectively. Second, with MODIS BBE data, the value of R2 is increased from 0.6 to 0.9, the RMSE and Bias are reduced by 1 and 2 magnitude orders, respectively. Finally, the Taklimakan Desert BBE was calculated using the revised GLASS BBE equation. The results showed that the BBE values are between 0.890 and 0.920 in the desert center, between 0.920 and 0.950 in the sparse vegetation areas, and between 0.950 and 0.980 in the oasis edges.

Distribution, abundance, and ecology of the threatened Gibson’s Big Sand Tiger Beetle (Cicindela formosa gibsoni Brown) in the Elbow Sand Hills of Saskatchewan

Gibson’s Big Sand tiger beetle, Cicindela formosa gibsoni Brown, occurs primarily in the Saskatchewan sand hills and was recently listed as threatened in Canada due to habitat loss caused by dune stabilization. Herein, we report on a 3-year population study initiated in 2016 to better understand the distribution, abundance, and ecology of C. f. gibsoni in the Elbow Sand Hills, a large active dune complex in southern Saskatchewan. Estimated adult population size for the dune complex varied from a low of 1106 (95% confidence interval (CI) 975–1237) to a high of 1474 (CI 1350–1598), possibly due to inter-annual variation in May–June rainfall. Adult abundance varied substantially between interdunal swales (0–137 individuals), with the highest numbers occurring in sparsely-vegetated habitats on the stoss side of the dune complex where the rate of encroachment by vegetation is highest. Approximately a third of the total population is concentrated within a relatively small area (~ 6 ha) in the northwestern region of the dune complex, although the specific cause for this localized distribution is not clear. Our findings suggest that the distribution of C. f. gibsoni within the dune complex is not limited by prey availability or larval habitat quality but is instead related to the amount of sparsely-vegetated habitat. We hypothesize that sparsely-vegetated areas allow beetles to shuttle between exposed and shaded microhabitats, thereby assisting in thermoregulation and maintenance of high body temperatures that are optimal for foraging.

Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia

Desertification is one of the main ecological environmental problems in Central Asia. To prevent and eradicate this problem, it is extremely urgent to monitor the long-term desertification process and assess the relative roles of its drivers. Based on an analytical hierarchy process, in this research, the spatiotemporal features of the desertification process were surveyed from 1982 to 2012 using four indicators. Further analysis was focused on determining the relative importance of multiple driving factors causing desertification in different ecosystems. The results revealed significant desertification expansion in the western part of Central Asia, with the most severe desertification occurring in eastern Xinjiang Province and the Ustyurt Plateau, with mean desertification indexes (DIs) as high as 0.8. According to a change-year analysis, mutation years of desertification were observed from 1993 to 2002 for most vegetation types. The desertification process for different vegetation types results from different major driving factors. Climatic factors, including decreased precipitation, increased temperature and drought, were the main drivers of desertification, especially for grasslands, forests and sparse vegetation. The desertification process of sparse vegetation and croplands was expanded and triggered by human activities: oil and gas exploration in the southern Ustyurt Plateau and agricultural abandonment in northern Kazakhstan. The results also indicated that after the collapse of the Soviet Union, rangeland abandonment in eastern Kazakhstan triggered desertification reversion in grasslands. Furthermore, due to inefficient irrigation water use, severe salt accumulation in croplands of the Amu Darya River delta resulted in desertification expansion in this region. In Central Asia, the desertification process in forests and areas of sparse vegetation was extremely sensitive to climatic variations, while that in croplands and grasslands was vulnerable to human activities. Therefore, regional governments should strive to reverse desertification to protect and improve this fragile, arid ecological environment.

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs

The aim of this study was to evaluate some parameters of water quality of semiarid reservoirs under different uses and occupation of the catchment’s soil. For this, the reservoirs Acauã and Boqueirão, belonging to the Paraíba do Norte river watershed and Middle and Upper course sub catchments, respectively, were studied. For this, water samples were collected in August, September and October 2016. From these samples, total and dissolved phosphorus, nitrate, nitrite, ammonia, chlorophyll, dissolved and suspended solids were analyzed. In addition, images of the Landsat 8 satellite were acquired for the calculation of the Normalized Difference Vegetation Index (NDVI), and for the supervised classification of the use and occupation of the sub catchments. Thus, it was observed that, in general, the Acauã reservoir presented values of phosphorus and nitrogen and solids larger than the Boqueirão reservoir, due to the greater urban area, even though it had a smaller total area of the basin. Both reservoirs presented low vegetation rates and high areas of sparse vegetation and exposed soil, increasing the propensity to soil erosion and the transport of nutrients from the basin to the reservoirs, making water quality worse or impossible.

Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms – the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22m for DTMs, <0.77m for CHMs) and root mean square error (RMSE) (<1.26m for DTMs, <1.93m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four forecasting models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.

Land Cover Status in the Koshi River Basin, Central Himalayas

Abstract: The Koshi River Basin is in the middle of the Himalayas, a tributary of the Ganges River and a very important cross-border watershed. Across the basin there are large changes in altitude, habitat complexity, ecosystem integrity, land cover diversity and regional difference and this area is sensitive to global climate change. Based on Landsat TM images, vegetation mapping, field investigations and 3S technology, we compiled high-precision land cover data for the Koshi River Basin and analyzed current land cover characteristics. We found that from source to downstream, land cover in the Koshi River Basin in 2010 was composed of water body (glacier), bare land, sparse vegetation, grassland, wetland, shrubland, forest, cropland, water body (river or lake) and built-up areas. Among them, grassland, forest, bare land and cropland are the main types, accounting for 25.83%, 21.19%, 19.31% and 15.09% of the basin's area respectively. The composition and structure of the Koshi River Basin land cover types...

The influence of wing morphology upon the dispersal, geographical distributions and diversification of the Corvides (Aves; Passeriformes).

New species are sometimes known to arise as a consequence of the dispersal and establishment of populations in new areas. It has nevertheless been difficult to demonstrate an empirical link between rates of dispersal and diversification, partly because dispersal abilities are challenging to quantify. Here, using wing morphology as a proxy for dispersal ability, we assess this relationship among the global radiation of corvoid birds. We found that species distributions are associated with wing shape. Widespread species (occurring on both islands and continents), and those that are migratory, exhibit wing morphologies better adapted to long-distance flight compared with sedentary continental or insular forms. Habitat preferences also strongly predict wing form, with species that occur in canopies and/or areas of sparse vegetation possessing dispersive morphologies. By contrast, we found no significant differences in diversification rates among either the migratory or habitat classifications, but species distributed in island settings diversify at higher rates than those found on continents. This latter finding may reflect the elevated dispersal capabilities of widespread taxa, facilitating the radiation of these lineages across insular areas. However, as the correlations between wing morphology and diversification rates were consistently weak throughout our dataset, this suggests that historical patterns of diversification are not particularly well reflected by present-day wing morphology.

DEM sourcing guidelines for computing 1 Eö accurate terrain corrections for airborne gravity gradiometry

In this paper we investigate digital elevation model (DEM) sourcing requirements to compute gravity gradiometry terrain corrections accurate to 1Eötvös (Eö) at observation heights of 80m or more above ground. Such survey heights are typical in fixed-wing airborne surveying for resource exploration where the maximum signal-to-noise ratio is sought. We consider the accuracy of terrain corrections relevant for recent commercial airborne gravity gradiometry systems operating at the 10Eö noise level and for future systems with a target noise level of 1Eö. We focus on the requirements for the vertical gradient of the vertical component of gravity (Gdd) because this element of the gradient tensor is most commonly interpreted qualitatively and quantitatively.Terrain correction accuracy depends on the bare-earth DEM accuracy and spatial resolution. The bare-earth DEM accuracy and spatial resolution depends on its source. Two possible sources are considered: airborne LiDAR and Shuttle Radar Topography Mission (SRTM). The accuracy of an SRTM DEM is affected by vegetation height. The SRTM footprint is also larger and the DEM resolution is thus lower. However, resolution requirements relax as relief decreases. Publicly available LiDAR data and 1arc-second and 3arc-second SRTM data were selected over four study areas representing end member cases of vegetation cover and relief.The four study areas are presented as reference material for processing airborne gravity gradiometry data at the 1Eö noise level with 50m spatial resolution. From this investigation we find that to achieve 1Eö accuracy in the terrain correction at 80m height airborne LiDAR data are required even when terrain relief is a few tens of meters and the vegetation is sparse. However, as satellite ranging technologies progress bare-earth DEMs of sufficient accuracy and resolution may be sourced at lesser cost. We found that a bare-earth DEM of 10m resolution and 2m accuracy are sufficient for achieving 1Eö accuracy in the terrain correction independent of relief or vegetation cover. For AGG systems operating at greater noise levels the 3arc-second SRTM is adequate for areas having tens of meters of relief and sparse vegetation and even for areas of greater relief and sparse vegetation if a constant elevation survey is flown at 80m above terrain peak.

Soil organic carbon stock explored by geo-spatial modeling at Sagar Island, India

Soil organic carbon (SOC) and organic carbon density (OCD) is an active indicator of soil fertility and productivity, and it fluctuates spatially and temporally. The main objective of the present study is to evaluate the spatial variability of SOC and OCD using geostatistics model as well as field methods at Sagar Island (Sundarban), India. Soil samples at the depths of 0–20 cm were collected from twenty plots at different season to estimate organic carbon concentrations and carbon density in the laboratory. Spatial distributions of SOC and OCD concentrations were estimated using ordinary kriging method. Remote sensing imagery and ground truth helped in identifying different features as well as land use/land cover correctly. Landuse are characterized by coastal water, sand, coastal wetland, salt marsh, agricultural fallow land, sparse vegetation, crop/paddy field, and mixed settlement area. The average value SOC were recorded as 2.80, 1.83, and 2.069 % in post-monsoon, pre-monsoon and monsoon season respectively. Semivariograms models of SOC and OCD in all seasons illustrated strong positive nugget values indicated short scale variability and sampling error. The coefficient of determination (R2) of the SOC model denoted as 0.71, 0.68 and 0.74 and for OCD calculated as 0.78, 0.67 and 0.83 at pre-monsoon, monsoon and post-monsoon season respectively. The results illustrated that mangrove forest portrayed highest amount of SOC concentration in both the pre-monsoon (1.05 kg/m2) and post-monsoon (0.94 kg/m2) season.

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation.